edgeimpulse 0.0.14

Python runner for real-time ML classification

Edge Impulse Linux SDK for Python

This library lets you run machine learning models and collect sensor data on Linux machines using Python. This SDK is part of Edge Impulse where we enable developers to create the next generation of intelligent device solutions with embedded machine learning. Start here to learn more and train your first model.

pip install edgeimpulse

runner.py

Implements the ImpulseRunner

use:

from edgeimpulse.runner import ImpulseRunner
import signal
runner = None

def signal_handler(sig, frame):
    print('Interrupted')
    if (runner):
        runner.stop()
    sys.exit(0)

signal.signal(signal.SIGINT, signal_handler)

...
runner = ImpulseRunner(modelfile)
model_info = runner.init()
...
res = runner.classify(features[:window_size].tolist())

Audio classification

from edgeimpulse.audio import AudioImpulseRunner
runner = AudioImpulseRunner('/path/to/your/model')
model_info = runner.init()
...
res = runner.classify(data)
print(res);

Classify audio from your microphone in real-time

from edgeimpulse.audio import AudioImpulseRunner
...
with AudioImpulseRunner('/path/to/your/model') as runner:
    model_info = runner.init()
    for res, audio in runner.classifier():
        print(res)

Image classification

from edgeimpulse.audio import ImageImpulseRunner
runner = ImageImpulseRunner('/path/to/your/model')
model_info = runner.init()
...
res = runner.classify(data)
print(res);

Classify images frames from your camera in real-time

from edgeimpulse.image import ImageImpulseRunner
import cv2

with ImageImpulseRunner('/path/to/your/model') as runner:
    model_info = runner.init()
    for res, img in runner.classifier():
        print(res)
        cv2.imshow('frame',img)
        cv2.waitKey()

examples:

/camera
/microphone

camera

Classifies frames grabbed directly from the webcam.

microphone

Classifies audio acquired directly from the audio interface.